{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance (2nd ed.)\n",
    "\n",
    "**Mastering Data-Driven Finance**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/py4fi_2nd_shadow.png\" width=\"300px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why Python for Finance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zen of Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Python Ecosystem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * 2.5 + 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# causes intentional error\n",
    "# log(1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.log(1)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finance and Python Syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "uuid": "1447b7bb-ed26-4c0f-9e0f-222dfd5d0c9b"
   },
   "outputs": [],
   "source": [
    "S0 = 100.  \n",
    "K = 105.  # <2\n",
    "T = 1.0  \n",
    "r = 0.05  \n",
    "sigma = 0.2  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "I = 100000  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "np.random.seed(1000)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "z = np.random.standard_normal(I)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "ST = S0 * np.exp((r - sigma ** 2 / 2) * T + sigma * math.sqrt(T) * z)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "hT = np.maximum(ST - K, 0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "C0 = math.exp(-r * T) * np.mean(hT)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "uuid": "84aab05d-40de-4ef1-b3a8-eb08f86e1662"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the European call option: 8.019.\n"
     ]
    }
   ],
   "source": [
    "print('Value of the European call option: {:5.3f}.'.format(C0))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the European call option 7.989.\n"
     ]
    }
   ],
   "source": [
    "%run bsm_mcs_euro.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time-to-Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "uuid": "e16924db-8402-4bcb-a9c7-d5753346c72a"
   },
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd  \n",
    "from pylab import plt, mpl  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')  \n",
    "mpl.rcParams['font.family'] = 'serif'  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "uuid": "22071d72-094b-4b51-aa39-cfa793d58623"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2138 entries, 2010-01-04 to 2018-06-29\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   .SPX    2138 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 33.4 KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('../../source/tr_eikon_eod_data.csv',\n",
    "                  index_col=0, parse_dates=True)  \n",
    "data = pd.DataFrame(data['.SPX']) \n",
    "data.dropna(inplace=True)  \n",
    "data.info()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "uuid": "304b2f50-5b81-4e88-8fa7-ea69995cf2fe"
   },
   "outputs": [],
   "source": [
    "data['rets'] = np.log(data / data.shift(1))  \n",
    "data['vola'] = data['rets'].rolling(252).std() * np.sqrt(252)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "uuid": "b4e61939-c7fe-4a1e-a8c9-6fd755edcf86"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['.SPX', 'vola']].plot(subplots=True, figsize=(10, 6));  \n",
    "# plt.savefig('../../images/ch01/spx_volatility.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Idioms & Paradigms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "uuid": "beca497d-4c20-4240-b003-4c79c26154be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.45 s ± 89.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "loops = 2500000\n",
    "a = range(1, loops)\n",
    "def f(x):\n",
    "    return 3 * math.log(x) + math.cos(x) ** 2\n",
    "%timeit r = [f(x) for x in a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "uuid": "931fd1fc-cc54-4045-802f-acb8f094a23f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.9 ms ± 2.6 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.arange(1, loops)\n",
    "%timeit r = 3 * np.log(a) + np.cos(a) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "uuid": "8d1602b3-a490-4d1a-97d9-112825d86185"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72.7 ms ± 1.08 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "import numexpr as ne\n",
    "ne.set_num_threads(1)\n",
    "f = '3 * log(a) + cos(a) ** 2'\n",
    "%timeit r = ne.evaluate(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "uuid": "6994f16a-1802-4abe-851d-a48e0ead154d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.7 ms ± 1.4 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "ne.set_num_threads(4)\n",
    "%timeit r = ne.evaluate(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data-Driven & AI-First Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data-Driven Finance "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This sub-section requires a Eikon license. Therefore no code is provided.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AI-First Finance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('http://hilpisch.com/tr_eikon_eod_data.csv',\n",
    "                   index_col=0, parse_dates=True)\n",
    "data = pd.DataFrame(data['AAPL.O'])  \n",
    "data['Returns'] = np.log(data / data.shift())  \n",
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = []\n",
    "for lag in range(1, lags + 1):\n",
    "    col = 'lag_{}'.format(lag)\n",
    "    data[col] = np.sign(data['Returns'].shift(lag))  \n",
    "    cols.append(col)\n",
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(gamma='scale')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data[cols], np.sign(data['Returns']))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Prediction'] = model.predict(data[cols])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Strategy'] = data['Prediction'] * data['Returns']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6));  \n",
    "# plt.savefig('../../images/ch01/AAPL_trading_strategy.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
